Does Municipal Efficiency Affect Citizen Satisfaction? An Empirical Analysis of Efficiency and Effectiveness in Victorian Local Government

Abstract

The performance of local governments continues to be a key concern for public policymakers around the world. While a substantial body of research has examined the supply side of municipal performance, particularly focusing on efficiency at both the service-specific and system-wide levels, comparatively little attention has been given to municipal effectiveness, or the demand-side perspective. This study aims to address this gap by empirically investigating the relationship between municipal efficiency, measured through operational efficiency estimates, and municipal effectiveness, measured through local resident satisfaction scores. The analysis focuses on the Victorian local government system, one of Australia’s seven states, over the period 2014/15 to 2017/18, with the goal of identifying whether a systematic link exists between efficiency and effectiveness in municipal performance.

Keywords

efficiency effectiveness local government performance Victoria

Introduction

The performance of municipal systems remains a key concern for public policymakers worldwide, particularly given their central role in delivering essential public services and infrastructure (Dakhilulloh et al., 2025; Nzama et al., 2023). In response, governments have introduced a range of reforms and interventions aimed at improving municipal outcomes, especially in terms of efficiency, accountability, and institutional quality (Gcabashe & Pillay, 2025; Roziq et al., 2025). These reforms have been particularly focused on enhancing financial sustainability and operational performance, which remain persistent challenges for many local governments (Makkonen et al., 2024). However, despite these efforts, the effectiveness of such initiatives has been uneven across jurisdictions, with many municipalities continuing to face performance and service delivery issues (Hendaris et al., 2025; Marawu & Utete, 2026).

These policy concerns are reflected in the academic literature, where considerable effort has been devoted to analyzing local government performance using diverse indicators (Hatry, 2018; Kearney, 2018). Within this body of research, two main strands are especially relevant. First, a substantial number of empirical studies have focused on municipal efficiency at both the overall system level and within specific service areas. For example, research on Victorian local governments in Australia has investigated operational efficiency using Data Envelopment Analysis (DEA), showing how factors such as administrative intensity can influence performance scores across councils over time (Tran & Dollery, 2023b). Similarly, empirical work on English local authorities has used DEA to assess efficiency in public service delivery, highlighting how performance assessment systems relate to operational performance in specific sectors such as education and social care (Biscione et al., 2025; Liarte et al., 2026). These and other studies contribute important insights into the operational performance of local authorities and the factors that influence efficiency outcomes across different contexts. In particular, it has highlighted the role of non-discretionary environmental conditions, such as demographic, economic, financial, social, and spatial characteristics, in shaping efficiency outcomes (Balaguer-Coll et al., 2015).

A second line of inquiry has examined municipal effectiveness, typically by assessing outcomes associated with service delivery, including citizen satisfaction (Andrews & Van de Walle, 2013). Compared with the extensive literature on efficiency, however, empirical research on effectiveness remains relatively limited, largely due to constraints in data availability (Drew et al., 2016). Existing studies have drawn on a variety of measures, including objective indicators (such as service disruptions and pricing changes; Kearney, 2018), expert evaluations from officials and practitioners (Lægreid et al., 2006), and survey-based measures capturing residents’ perceptions of specific services and overall council performance (Van Ryzin, 2015).

Despite these advances, very few studies have examined the potential link between efficiency and citizen satisfaction. While prior research has explored various aspects of performance management (Andrews et al., 2011), this specific association remains largely under-investigated. This paper seeks to address this gap by examining whether more efficient councils are associated with higher levels of resident satisfaction in the Victorian State for the period 2014/15–2017/18. Specifically, we estimate efficiency scores for Victorian councils and then analyze their relationship with satisfaction measures. To achieve this, we apply bootstrapped DEA to derive efficiency estimates and subsequently assess their association with satisfaction outcomes.

Empirically analyzing how residents respond to municipal performance presents several challenges (Andrews et al., 2011; Higgins, 2005; Ma, 2017; Van Ryzin, 2007). Measures of service performance are often incomplete or imprecise, and even when reliable indicators are available, they may not align with public perceptions. Moreover, the processes through which residents form judgments about local government performance are not well understood. Nevertheless, given the importance of effectiveness as a dimension of public sector performance, further empirical investigation is warranted.

The structure of this paper is as follows. Section 2 reviews the literature on municipal effectiveness, while Section 3 provides an overview of the institutional characteristics of the Victorian municipal context. Section 4 details the methodology used in the analysis. The empirical findings are presented in Section 5, followed by a discussion of the results in Section 6. Finally, Section 7 concludes the paper, highlighting the main findings and their implications.

Effectiveness in Local Government Performance

In contrast to efficiency analysis, which primarily examines the relationship between inputs and outputs, studies of effectiveness emphasize the link between public service outputs and broader outcomes such as quality of life and citizen satisfaction. Effectiveness research often considers how the delivery of services and governance processes influence local residents’ perceptions of government performance (Song & Meier, 2018; G. G. Van Ryzin, 2004). Citizen satisfaction with municipal services is widely used in the literature as an outcome measure that reflects the effectiveness of local governance and service quality (Shahzada et al., 2024). According to the expectancy-disconfirmation model, satisfaction arises when citizens’ expectations about service delivery are met or exceeded, highlighting the importance of perceived performance in evaluating outcomes (Qin et al., 2025). Studies also show that local government decisions and policies play a significant role in shaping citizens’ sense of belonging and satisfaction with their communities, linking service outcomes to perceptions of effectiveness (Pazos-García et al., 2025).

However, the use of these measures is not without limitations (Jakobsen & Kjaer, 2016; Olsen, 2015).

Research on local government effectiveness has developed across four main dimensions: citizen satisfaction with service delivery, satisfaction with local amenities, perceptions of problem-solving capacity, and evaluations of local democratic processes (Hansen, 2015). Although some advances have been made, findings remain inconclusive. For example, studies by Andrews and Van de Walle (2013), Ma (2017), and others have explored the influence of performance management on resident satisfaction, but results have been mixed. Similarly, the relationship between municipal size and satisfaction has been widely examined (Boyne, 1996; Drew et al., 2016; Drew & Dollery, 2016; Hansen, 2015; Lassen & Serritzlew, 2011; G. G. Van Ryzin, 2004), yet no clear consensus has emerged.

The reliance on constructed output–outcome indicators or survey-based satisfaction measures as proxies for effectiveness presents several challenges (Olsen, 2015). In particular, discrepancies often arise between objective performance data and subjective citizen perceptions. Factors such as expectations, response bias, and measurement error can reduce the reliability of survey results (James & John, 2007). In addition, demographic and socio-economic characteristics, including age, gender, income, and cultural background, may systematically influence reported satisfaction levels (Kelly & Swindell, 2002). While some methodological techniques can mitigate these issues, they cannot fully eliminate them (Jakobsen et al., 2019).

Notwithstanding these limitations, this study employs annual citizen satisfaction survey data from the Victorian local government system as an indicator of municipal effectiveness. Victoria provides a particularly suitable context, as it is the only Australian state, and one of the few jurisdictions internationally that mandates consistent, standardized annual surveys across all local councils. Accordingly, this study utilizes data from the Victorian Local Government Community Satisfaction Survey for 79 councils over the period 2014–2018 (Victoria State Government, 2018).

Victorian Local Government

In contrast to many advanced economies, Australian local governments have historically concentrated on property-related functions, such as waste collection, wastewater management, local road maintenance, and land-use planning, rather than people-focused services like education, healthcare, and policing (Dollery et al., 2006). In recent decades, however, their responsibilities have expanded to include a broader range of community-oriented services, including aged care and social support. While the roles and authority of municipalities are defined by state and territory Local Government Acts, there is relatively little variation in service provision across jurisdictions (Dollery & Marshall, 1997; Productivity Commission, 2022). Instead, differences in local government activities are more strongly shaped by the specific demographic and spatial characteristics of the regions in which they operate.

Victoria, the second most populous state in Australia, had approximately 5.5 million residents and 1.9 million households in 2016 (Australian Bureau of Statistics [ABS], 2016a; Municipal Association of Victoria [MAV], 2016). At that time, the state comprised 79 general-purpose councils employing around 51,300 people and generating nearly $9 billion in revenue. Of this, $8.1 billion was sourced from property taxes, fees, and charges, while $638 million came from intergovernmental grants (MAV, 2016). Consequently, expansions in local service provision are largely dependent on increases in property-based revenue. However, Victorian councils operate under a state-imposed cap on annual property tax increases, commonly referred to as “rate capping.” Despite operating within a uniform legislative framework, councils differ in the range and level of services they provide, largely reflecting variations in demographic and geographic conditions.

Of the 79 municipalities in Victoria, 54 are classified as metropolitan and 25 as rural (Department of Infrastructure and Regional Development [DIRD], 2015). A municipality is classified as urban if it has a population exceeding 20,000, a population density greater than 30 persons per square kilometer, or at least 90% of its population residing in urban areas. Councils that do not meet these criteria are designated as rural.

The challenges faced by rural and urban councils differ markedly. Rural councils typically cover areas more than nine times larger than urban councils, yet have population densities that are, on average, 93 times lower. Socio-economic disparities are also evident: average wages in rural areas are around 80% of those in urban areas, a higher proportion of rural residents are aged over 65, and rural populations declined between 2009 and 2015, in contrast to rapid growth in urban areas (ABS, 2016b; Victoria Local Government Grants Commission, 2016).

These structural differences significantly shape service delivery responsibilities. In many cases, small and remote rural councils act as “providers of last resort,” delivering services beyond the conventional scope of local government, including general retail, aged care, housing, banking, and postal services (Dollery et al., 2010). At the same time, rural councils face more limited revenue-raising capacity, particularly in generating income from sources such as parking fees and fines. As a result, they tend to rely more heavily on intergovernmental grants to sustain service provision

Methodology

This paper investigates the relationship between the eficiency of local councils and the satisfaction of their residents. Specifically, we aim to test whether higher council efficiency scores are associated with greater resident satisfaction, holding other factors constant. To achieve this, our empirical strategy employs two complementary methods of analysis.

Data Envelopment Analysis (DEA)

Empirical Model

In the first stage, we estimate the efficiency scores of individual councils using the standard Data Envelopment Analysis (DEA) approach developed by Charnes et al. (1978), which assumes constant returns to scale (CRS). In practice, however, not all production units operate at an optimal scale due to scale effects or external factors. To address this, Banker et al. (1984) extended the DEA model to incorporate variable returns to scale (VRS), allowing the estimation of pure technical efficiency without the influence of scale effects. Accordingly, we adopt the DEA VRS approach to estimate the efficiency scores of Victorian councils (See Appendix A1 for the linear programming model).

DEA-estimated efficiency scores are generally sensitive to outliers and extreme values because the DEA approach does not consider random variation in the underlying data process. Narbón-Perpiñá et al. (2019) suggest that empirical researchers consider partial frontier approaches, such as the order-m frontier (Cazals et al., 2002; Daraio & Simar, 2005) or bias-corrected DEA estimators (Kneip et al., 2008), to reduce estimation bias and produce more robust results. In this study, we first examine (a) whether outliers exist in our dataset and (b) if present, apply adjustments to prevent their extreme performance from disproportionately influencing other councils. The procedure for identifying outliers follows Thanassoulis (1999) and is presented in Appendix A2.

A further drawback of the non-parametric DEA framework is the challenge it poses for conducting statistical inference (Narbón-Perpiñá et al., 2019). Consequently, efficiency estimates derived from conventional DEA may be biased, as they do not incorporate sampling variability or stochastic disturbances (Simar & Wilson, 2000). To overcome this issue, we apply the bootstrap procedure proposed by Simar and Wilson (1998) to evaluate the sensitivity of efficiency estimates to sampling fluctuations. This approach enables the estimation of precision indicators, including bias, variance, and confidence intervals.

Before applying bootstrapping, an independence test between efficiency and external factors should be performed (Peyrache & Coelli, 2009; Wilson, 2003). If the p-value is <.05, the null hypothesis of non-correlation is rejected, and bootstrapping is appropriate; if it is greater than 0.05, bootstrapping is unnecessary. In our dataset, the Chi-square test produced a p-value of .000, leading to the rejection of the null hypothesis and supporting the application of the bootstrap method. Using 2,000 pseudo-sample replications, the bootstrapped efficiency scores are observed to be slightly less than unity.

Regarding input-output orientation, we employ an input-oriented DEA approach, which estimates frontier efficiency by minimizing inputs while keeping outputs constant. This approach is particularly suitable for Victorian councils, as the scale of local services largely depends on factors such as the population/the number of households and commercial establishments served, the extent of road networks maintained, kerbside bin collection requests, and similar indicators. Councils are therefore expected to minimize input resources required to meet existing service demand while maintaining service quality.

This input-oriented method has been widely used in recent research on Australian local government (Tran & Dollery, 2021, 2023b). The use of total households and businesses as a measure of service demand is appropriate because Victorian local government, like other Australian local authorities, primarily provides “services to property,” including local roads, bridges, footpaths, sewage, and water, rather than “services to people” as in most other developed countries. Drew and Dollery (2014) demonstrate that total households and businesses, rather than population alone, best capture the demand for these local services.

Input and Output Variables

The choice of inputs and outputs is a critical consideration in DEA analysis, often constrained by data availability (Narbón-Perpiñá et al., 2019). We use the total number of assessments, including both households and businesses, as the primary output measure because it offers greater stability and accuracy compared with using the total population of a municipality (Drew et al., 2017; Drew & Dollery, 2014). The total length of roads is also included as an output, consistent with prior studies (e.g., Drew et al., 2017; Fogarty & Mugera, 2013). Waste collection is represented by kerbside bin collection requests, which serve as a practical proxy due to data constraints. Finally, the value of infrastructure, encompassing property, plant, and equipment, is used to capture resources devoted to leisure and recreational facilities for the community. These outputs have been widely employed in empirical research on local government performance (e.g., studies by Balaguer-Coll et al., 2019; Geys & Moesen, 2009; Kalb et al., 2012; Šťastná & Gregor, 2015).

For the input, we use total municipal expenditure, which covers staff costs, spending on recreation, environment, health, and community services, as well as road maintenance and related expenses. While staff expenditure can sometimes be treated as a separate input (Drew et al., 2017), data limitations prevent its disaggregation in this study, so it is incorporated within total expenditure. This input has also been widely used in the literature of efficiency studies (Alonso & Andrews, 2019; Drew et al., 2017; Narbón-Perpiñá et al., 2019).

In summary, the DEA model is specified with one input and four outputs to estimate the frontier efficiency of local councils, drawing on a Victorian panel dataset spanning 2014/15–2017/18.

Data Source and Data Analysis

Data for this study were sourced from the Victorian Local Government (2019). The input in our DEA model is total municipal expenditure, which includes all outlays required to deliver local services, such as staff costs. The outputs comprise: (i) households and businesses, representing the total number served by Victorian local government; (ii) roads, measured in kilometers; (iii) kerbside bin collection requests received annually; and (iv) the value of property, plant, and equipment allocated to recreational facilities.

We employed a pooled data structure to measure efficiency for Victorian councils over the 3-year period (Fried et al., 2008). This approach allows periodic efficiency scores to be calculated for each council against the same benchmark, enabling the analysis of performance trends across the reported periods (Villano & Tran, 2019).

To ensure the suitability of our chosen inputs and outputs, we conducted an isotonicity test to verify that increases in inputs correspond to increases in outputs (Thanassoulis et al., 2008). Using a Pearson correlation test, results (Table A3, Appendix 1) show a positive relationship between inputs and outputs at the 1% significance level, confirming the appropriateness of our selected models.

The Effect of Community Satisfaction on Municipal Performance

To examine the effect of community satisfaction on municipal performance, we conducted a regression analysis framework with time-fixed effects. This approach allows us to assess the association while controlling for key variables such as population measures and environmental characteristics.

The empirical models are specified as follows:

Model 1:

Ln ({OS}_{t}) = β_{0} + β_{1} {EFF}_{t-1} + β_{2} \ln (PopSize) + β_{3} \ln (PopDen) + β_{4} Rural+ β_{s} Z_{s} + ε

Model 2:

Ln ({OS}_{t}) = β_{0} + β_{1} {EFF}_{t-1} + β_{2} \ln (PopSize) + β_{3} {(lnPopSize)}^{2} + β_{4} \ln (PopDen) + β_{5} Rural+ β_{s} Z_{s} + ε

where ln(OS_t) represents the natural logarithm of resident satisfaction in period $t$ . This variable captures overall community satisfaction, expressed as a value within zero and 100. It was selected because nearly all residents consistently responded to this overall performance index, whereas other single indicators were not consistently rated across all Victorian councils. To facilitate comparison with the results of Drew et al. (2016), Models 1 and 2 are also estimated without including efficiency, allowing us to explore the potential curvilinear relationship between population size and resident satisfaction.

Efficiency scores: In prior studies of local government efficiency, DEA scores were typically regressed against non-discretionary variables in a second stage to investigate the influence of external factors on council performance. In contrast, in this study, the efficiency score of each local council ( $E F F_{t - 1}$ ) is treated as an independent variable measured at period $t - 1$ , which is then regressed against resident satisfaction. These scores range between zero and one. Lagged values are employed because community surveys are conducted in the first 3 months of each year, whereas municipal performance data are only publicly released in the fourth quarter.

Endogeneity may arise when efficiency is used as an explanatory variable, as it may correlate with other explanatory variables and potentially bias the results. To test for this, we first ran a regression with efficiency as the independent variable and population measures and other controls as explanatory variables. The results indicated strong correlations between efficiency and population size, population density ( $p = 0.000$ ), and other control variables ( $p < 0.05$ ), confirming potential endogeneity. To address this, we employed two-stage least squares (2SLS), an extension of OLS suitable when at least one explanatory variable is endogenous (Wooldridge, 2016). The variance method with bootstrapping techniques is used to generate more robust findings. The standard and bias-corrected efficiency scores are used in separate model specifications. After running 2SLS, an exogeneity test confirmed that all independent variables were exogenous ( $p = 0.44 > 0.05$ ), validating the robustness of our findings.

Population measures: We include $\ln (P o p S i z e)$ , ${(\ln (P o p S i z e))}^{2}$ , and $\ln (P o p D e n)$ to capture the effects of population size, its quadratic term, and population density, respectively. Population size and density are widely used in studies of local government performance and resident satisfaction. Drew et al. (2016) found positive associations between population size and satisfaction for metropolitan councils, but not across all Victorian councils using 2010 data, highlighting the need for updated empirical analysis. The quadratic term is included based on prior evidence of curvilinear relationships between community satisfaction and municipal performance (Mok, 2020; Tran & Dollery, 2020). Population density may influence scale, scope, and satisfaction (Stipak, 1979; Tran & Dollery, 2020, 2023a), with previous findings suggesting a weak negative correlation with resident satisfaction (Drew et al., 2016).

Control variables: The dummy variable Rural equals 1 for non-metropolitan municipalities and 0 for metropolitan councils, following the Victoria AuditorGenerals Office (VAGO, 2018) classification. Additional controls ( $Z$ ) account for other factors influencing resident satisfaction, following insights from Drew et al. (2015) and Tran and Dollery (2023 a,b). These include cultural and demographic characteristics: the percentage of Aboriginal and Torres Strait Islander (ATSI) residents, percentage of non-English speaking background (NESB) residents, percentage unemployed, percentage of residents under 15 years, percentage over 65 years (potential pension recipients), and percentage of disabled residents. These variables have been widely used in the municipal literature (Mok, 2020; Tran & Dollery, 2020, 2023b).

A summary of all variables used in the DEA and regression models is presented in Table 1.

Table 1.

Summary of Variables Used in the Models, 2014/15–2017/18 (n = 79).

Variable	Definition	Mean	Standard deviation
Standard DEA model
Input
Total expenditure ($1,000)	Expenditure covering all expenses for services provision	101,427	79,232
Outputs
Assessments	Total households and businesses of each council	33,849	29,389
Total road (km)	The length of total road	1,676	1,241
Waste requests	Number of kerbside bin collection requests received	3,296	4,299
Infrastructure value ($1,000)	Value of property, plant and equipment excluding land	637,201	484,915
Regression model for the relationship between Overall satisfaction and efficiency scores
Dependent variable
Community satisfaction (ln)	Natural log of satisfaction	4.09	2.04
Independent variables
Efficiency scores (%)	Estimated using the DEA model	–	–
Population size (ln)	Natural log of population size	10.69	1.19
Population density (ln)	Natural log of population density	3.58	1.70
Rural dummy	1 for Rural and 0 for Urban	n/a	n/a
Control variables
Age under 15 (%)	Percentage of people less than 15 years old	17.9	0.063
Age over 65 (%)	Percentage of people greater than 65 years old	18.1	0.059
ATSI (%)	Percentage of Aboriginal and Torres Straight Islanders people.	1.12	0.78
NESB population (%)	Percentage of people speaking a language other than English at home	14.68	15.93
Unemployed (%)	Percentage of Unemployed people	5.81	1.61
Disability (%)	Percentage of persons receiving a government disability pension	12.11	1.30

Empirical Results

In this section, we present the empirical results addressing the research questions. Section 5.1 reports the efficiency scores of Victorian local councils derived from standard and bootstrapped DEA models, while Section 5.2 examines the association between these efficiency scores and resident satisfaction, controlling for external factors.

Municipal Performance in Victoria

Table 2 shows that the average standard DEA efficiency score of Victorian local councils is 0.795, indicating that, on average, councils could potentially improve performance by approximately 24.1% to reach full frontier efficiency. This is lower than the average efficiency reported for New South Wales councils (0.902–0.949; Drew et al., 2015), but higher than that of local municipalities in Bosnia and Herzegovina (0.712; Soko & Zorič, 2018). Over the period 2014/15 to 2017/18, there is a declining trend in efficiency, with the average score decreasing from 0.813 to 0.776. Hotelling’s test confirms that this change is statistically significant at the 1% level.

Table 2.

Efficiency Scores of Victorian Local Councils, 2014/15-2017/18 (n = 79).

Efficiency scores	2014/15	2015/16	2016/17	2017/18	Average
Standard Efficiency scores
Mean	0.813	0.801	0.791	0.776	0.795
Standard deviation	0.131	0.133	0.140	0.127	0.124
Min	0.528	0.524	0.497	0.504	0.515
Max	1.000	1.000	1.000	1.000	1.000
Hotelling test (F value) across years			7.10***
Bias-corrected Efficiency scores
Mean	0.779	0.763	0.755	0.738	0.759
Standard deviation	0.115	0.112	0.124	0.108	0.107
Min	0.501	0.482	0.443	0.466	0.473
Max	0.982	0.976	0.974	0.958	0.964
Upper	0.806	0.795	0.784	0.769	0.788
Lower	0.746	0.728	0.744	0.753	0.743
Hotelling test (F value) across years			14.06***
Hotelling test between standard and bias-corrected efficiency scores
2014/15			102.91***
2015/16			76.60***
2016/17			96.85***
2017/18			86.42***
Average			111.52***

***

denotes the significance level at the 1%.

Recognizing that standard DEA scores may be biased due to sampling variation and random errors, we applied a bootstrapping approach. The bias-corrected efficiency scores (Table 3) average 0.759, lower than the standard DEA scores, and also exhibit a statistically significant decreasing trend over time. Bootstrapping reduced the variability of efficiency estimates across councils and provided 95% confidence intervals (ranging from the 2.5% to 97.5% quantiles), indicating consistent and robust results. Hotelling tests further confirm that differences between standard and bootstrapped scores are statistically significant at the 1% level. Figure 1 illustrates that bootstrapped efficiency scores have narrower dispersion, and no outliers are present, confirming the reliability of our models.

Table 3.

Total Expenditure Saved of Victorian Local Councils, 2014/15–2017/18 ($1,000) (n = 79).

Expenditure statistics	2014/15	2015/16	2016/17	2017/18	Average
Mean^a	19,778	21,148	23,132	24,337	20,263
Standard deviation	24,590	26,217	29,506	28,937	26,198
Min	134	69	395	80	51
Max	184,222	197,782	226,163	216,506	206,168
Efficient councils^b	10	9	9	6	2

Not include efficient councils that have no expenditure surplus.

Councils are efficient in using expenses for service provision and thus do not need to save expenditure.

Figure 1.

Box plot of standard DEA and bootstrapped DEA results.

Regarding input savings, the standard DEA results (Table 3) suggest that inefficient councils could save, on average, AUD 20.26 million annually while maintaining current service levels. Although total potential savings increase over time, the number of inefficient councils declines, reflecting heterogeneity in operational characteristics and environments across Victorian councils.

The Effect of Municipal Performance on Community Satisfaction

Regression models were estimated using both standard and bias-corrected efficiency scores. For robustness, we focus on the results using bias-corrected efficiency, while standard DEA results are provided in Table A4 (Appendix 1) for reference. Coefficients for standard efficiency models share the same direction as bias-corrected models but generally have smaller magnitudes and R² values, reflecting the enhanced robustness from bootstrapping and 2SLS estimation.

Models 1 and 2 were first estimated without efficiency scores to examine the relationship between population size and resident satisfaction. Table 4 shows that population size is negatively correlated with satisfaction at the 1% significance level, while the positive coefficient of quadratic term demonstrates a non-linear U-shaped relationship. This contrasts with Drew et al. (2016), who observed a reverse U-shaped pattern using 2010 cross-sectional data. Using our panel data (2014/15–2017/18) provides updated, reliable insights into resident perceptions. Population density is positively related to satisfaction, whereas the dummy variable for rural councils is negative, indicating lower satisfaction in non-metropolitan areas.

Table 4.

Regression Results of Overall Satisfaction (t) Against Population Measures for 2014/15–2017/18.

Overall satisfaction _t	Model 1			Model 2
Overall satisfaction _t	Coefficients	Robust Standard error	t Value	Coefficients	Robust standard error	t Value
LnPopSize	−0.054***	0.014	−3.74	−0.51***	0.12	−4.18
(LnPopSize)²	–	–	–	0.022***	0.006	3.78
LnPopDensity	0.0310***	3.06	4.28	0.034***	0.010	3.24
Rural	−0.105***	−3.22	−1.67	−0.080**	0.031	−2.56
Control variables^a	Yes			Yes
R ²	.416			.441
p Value	.0000			.0000
n	237			237

Including contextual factors including people less than 15 years old (%), people greater than 65 years old (%), Aboriginal and Torres Straight Islanders (ATSI) (%), unemployed persons (%) and disabled people (%).

***

and ** denotes the significance levels at 1% and 5%, respectively at the two-tail test.

Table 5 presents results incorporating bias-corrected efficiency scores. The relationship between community satisfaction and municipal performance is positive and statistically significant (1% level) and shows marginal significance in Model 2 (one-tailed p = .065), indicating that efficiency positively influences satisfaction even when population size is modeled quadratically. A 10% increase in efficiency corresponds to a 1.7- to 1.4-point increase in satisfaction scores in Models 1 and 2, respectively, holding other factors constant.

Table 5.

Regression Results of Overall Satisfaction (t) Against Bias-Corrected Efficiency Scores (t−1) for 2014/15–2017/18.

Overall satisfaction_t	Model 1			Model 2
Overall satisfaction_t	Coefficients	Bootstrapped^a Standard error	Z value	Coefficients	Bootstrapped Standard error	Z value
Efficiency_t-1	0.531***	0.150	3.53	0.309⁺	0.204	1.51
LnPopSize	−0.070***	0.015	−4.52	−0.353**	0.177	−2.00
(LnPopSize)²	–	–	–	0.014⁺⁺	0.009	1.65
LnPopDensity	0.045***	0.010	4.51	0.041***	0.011	3.79
Rural	−0.049	0.038	−1.29	−0.056	0.037	−1.53
Control variables^b	Yes			Yes
Constant	4.638***	0.157	29.62	6.169***	0.967	6.38
R ²	.4176			.4429
p Value	.0000			.0000
n	237			237

Bootstrapped with 2,000 replications.

***

and ** denotes the significance levels at 1% and 5%, respectively at the two-tail test. ⁺⁺ & ⁺ denotes the significance level at 5% and 10%, respectively for the one-tail test.

In the presence of efficiency scores, population size remains negatively associated with satisfaction, while the quadratic term retains a positive but lower significance, reinforcing the U-shaped relationship. Population density continues to show a strong positive effect. The rural dummy becomes less significant when efficiency is included (one-tailed p = .06), suggesting that efficiency partly moderates the urban–rural satisfaction gap.

Overall, these findings confirm that technical efficiency of local councils positively contributes to resident satisfaction, indicating that Victorian local authorities can enhance community satisfaction by improving operational efficiency.

Discussion

Australia’s large geographic scale and dispersed settlement patterns create substantial variation in the operating environments of local governments, particularly between rural and metropolitan councils. Rural authorities frequently act as primary providers of essential services, such as water supply, aged care, and community support, due to limited market provision in sparsely populated areas. However, lower population densities and constrained fiscal capacity often result in comparatively lower service quality and infrastructure standards, alongside a narrower and less diversified revenue base (Dick-Sagoe, 2020; OECD, 2025). These structural constraints also imply that rural residents are generally more sensitive to increases in local taxes and user charges, reflecting lower average income levels and limited economic opportunities.

Consistent with these expectations, our empirical results indicate that residents in rural councils report lower levels of satisfaction compared with their urban counterparts. However, this relationship weakens once efficiency is measured using bias-corrected scores, suggesting that part of the rural–urban disparity can be explained by differences in operational performance rather than location alone. This aligns with prior research showing that efficiency improvements can partially offset structural disadvantages faced by smaller or rural jurisdictions (Afonso & Venâncio, 2020; Bel & Warner, 2015).

The regression results further reveal a positive association between municipal efficiency and resident satisfaction, supporting the argument that more efficient service delivery enhances perceived performance and community outcomes. This finding is consistent with broader public administration literature, which links technical efficiency in service provision to improved citizen evaluations of government performance (Andrews & Van de Walle, 2013; Van Ryzin, 2004). Nevertheless, the reduced statistical significance observed when incorporating the quadratic population term suggests that the effect of efficiency is conditioned by local demographic structure, particularly population size.

Population density consistently shows a positive and significant effect on resident satisfaction across all models. In addition, the identified U-shaped relationship between population size and satisfaction indicates that both very small and very large councils may experience higher satisfaction levels, albeit for different reasons, such as community cohesion in smaller areas and service diversity in larger ones. This contrasts with earlier cross-sectional findings and highlights the value of panel data in capturing dynamic relationships over time.

Importantly, our results suggest that efficiency plays a moderating role in the relationship between population characteristics and resident satisfaction. More efficient councils appear better able to translate demographic advantages into improved service outcomes, thereby enhancing community perceptions. This reinforces the view that efficiency is not only a measure of internal performance but also a key mechanism through which local governments can improve effectiveness. Future research could extend this analysis by examining longer time horizons and incorporating additional institutional and socio-economic variables to better understand the drivers of efficiency across different municipal contexts. Further work is also needed to explore how variations in governance arrangements, fiscal capacity, and service delivery models influence both efficiency and citizen satisfaction outcomes.

Conclusion

The paper has contributed to the local government literature by investigating the association between municipal performance and community satisfaction across Victorian councils from 2014/15 to 2017/18. A two-stage least squares (2SLS) model was employed to (a) estimate the efficiency of councils in service provision to community given their service quality, and (b) examine if higher operational efficiency is associated with greater resident satisfaction, accounting for population measures.

Our paper found that Victorian councils were operating inefficiently, with efficiency scores exhibiting a declining trend over the study period. Our analysis indicates that councils could potentially save approximately $20.236 million annually on service provision costs if they achieved best-practice efficiency. Most importantly, the results provide robust evidence of a positive relationship between operational efficiency and resident satisfaction, indicating that councils with higher efficiency scores tend to have more satisfied residents.

This study tackles a critical question in local government studies: is municipal efficiency related to municipal effectiveness? By proxing efficiency through operational performance and effectiveness through resident satisfaction, we demonstrate that the positive link between efficiency and satisfaction persists over time for Victorian councils. Methodologically, the study breaks new ground by employing 2SLS regression to mitigate endogeneity concerns and confirm the robustness of the results. We also found that efficiency moderates the association between council size by population and community satisfaction, with population density positively affecting satisfaction, while residents in rural councils report lower satisfaction than their urban counterparts. This aligns with Drew and Dollery (2016), who suggest that smaller rural councils may be less satisfying due to higher transparency and closer personal familiarity between residents and council staff.

From a policy perspective, our findings suggest that improving municipal efficiency is not only a desirable objective on its own but also a means to enhance resident satisfaction. Public policy interventions that strengthen operational efficiency can therefore improve municipal effectiveness, although smaller rural councils may require additional strategies to achieve similar gains. Overall, this study reinforces the importance of performance-based management in local government and highlights the practical benefits of aligning efficiency improvements with resident satisfaction outcomes.

Footnotes

Appendix 1 ORCID iD

Carolyn Thi Thanh Dung Tran

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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